Thermodynamic and kinetic interpretation of the glass-forming ability of Y-containing Cu-Zr-Al bulk metallic glasses

Malekan, Mehdi; Rashidi, Reza; Shabestari, S.G.; Eckert, Jürgen

doi:10.1016/j.jnoncrysol.2021.121266

Cited by 10 publications

(4 citation statements)

References 34 publications

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“… Alloy composition Glass transition temp Crystallization temp Liquidus temp Measured critical diameter (from Ref 90 ) Predicted critical diameter (this work) Measured and predicted discrepancy% Refs. Cu 50 Zr 43 Al 7 713.0 781.0 1205.0 10.0 10.8 + 8 90 (Cu 50 Zr 43 Al 7 ) 98 Y 2 696.0 770.0 1165.0 15.0 14.3 − 4.7 (Cu 50 Zr 43 Al 7 ) 96 Y 4 679.0 715.0 1150.0 12.0 11.3 − 6 (Cu 50 Zr 43 Al 7 ) 94 Y 6 665.0 703.0 1145.0 8.0 8.0 0.0 …”

Section: Resultsmentioning

confidence: 98%

“…In order to validate the established model (the one including 13 features and characteristic temperatures), the measured D max as the glass-forming ability of four different alloys along with the related data were extracted from reference 90 . The data of alloys entered into the model, and then the predicted D max is compared with the experimental results, as shown in Table 2 .…”

Section: Resultsmentioning

confidence: 99%

“…For validation of the model, the characteristic temperatures of the four alloys studied in the previous work 90 were used as inputs. The predicted D max of these alloys was compared with the measured values of D max .…”

Section: Methodsmentioning

confidence: 99%

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Thermodynamically-guided machine learning modelling for predicting the glass-forming ability of bulk metallic glasses

Ghorbani

Askari

Malekan

et al. 2022

Sci Rep

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Glass-forming ability (GFA) of bulk metallic glasses (BMGs) is a determinant parameter which has been significantly studied. GFA improvements could be achieved through trial-and-error experiments, as a tedious work, or by using developed predicting tools. Machine-Learning (ML) has been used as a promising method to predict the properties of BMGs by removing the barriers in the way of its alloy design. This article aims to develop a ML-based method for predicting the maximum critical diameter (Dmax) of BMGs as a factor of their glass-forming ability. The main result is that the random forest method can be used as a sustainable model (R2 = 92%) for predicting glass-forming ability. Also, adding characteristic temperatures to the model will increase the accuracy and efficiency of the developed model. Comparing the measured and predicted values of Dmax for a set of newly developed BMGs indicated that the model is reliable and can be truly used for predicting the GFA of BMGs.

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Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

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Thermodynamically-guided machine learning modelling for predicting the glass-forming ability of bulk metallic glasses

Ghorbani

Askari

Malekan

et al. 2022

Sci Rep

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“…Recently, there has been a significant focus among researchers on the advancement of crystalline or amorphous solid electrolyte materials that exhibit exceptional electrical properties. As a result, glasses composed of lead borosilicate have been recognized as electrolyte materials with exceptional efficiency (5) . Under PbO's substantial band gap, glasses containing it exhibit exceptional transparency, even in the ultraviolet (UV) spectrum, thereby enhancing their resistance to moisture.…”

Section: Introductionmentioning

confidence: 99%

Characterization of a Novel System of Bismuth Lead Borosilicate Glass Containing Copper

Krishna,

Guntu,

Shareefuddin

et al. 2024

IJST

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Objectives: The primary objective of the present investigation was to examine the impact of Bi3+ ions on the optical, radiation shielding, dielectric and structural characteristics of lead borosilicate glasses doped with CuO. Method: In this view, we planned to utilize the conventional rapid melt quenching method to produce glasses with the following chemical composition: 25 PbO + 15 B2O3 + 0.1 CuO + (59.9-x) SiO2 :x Bi2O3 (0≤x≤12). Findings: The samples' non-crystalline characteristics were validated through XRD and SEM analysis, respectively, while their glass-forming capabilities were assessed through DTA studies. Utilizing the FT–IR and Raman analyses, the numerous structural units were determined. According to the findings of the FT-IR, and Raman analyses, the degree of disorder in the glass network increased. By means of optical absorption experiments, the optical properties of glasses were determined. The results obtained from optical absorption spectral investigations indicated that the concentration of octahedral Cu2+ ions increased gradually as the Bi2O3 concentration increased to 12 mol %. The dielectric properties of the glasses, suggested that the dielectric constant values of glasses containing Bi2O3 in concentrations below 12 mol% increase gradually. Additionally, the radiation shielding properties of the glasses are investigated. The values of radiation protection ability found to be purely function of Bi2O3 concentration. Novelty: The results suggest that the glasses' thermal stability, dielectric constant, optical band gap and radiation shielding ability values purely function of Bi2O3 concentration. Keywords: Borosilicate glasses, Thermal stability, Optical absorption, Radiation shielding properties, Dielectricsq

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Synthesis by Mechanical Alloying and Characterization Fe73Si22B5 Alloy: Amorphization Evaluation

Nascimento,

de Melo Costa

2023

Braz J Phys

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Thermodynamic and kinetic interpretation of the glass-forming ability of Y-containing Cu-Zr-Al bulk metallic glasses

Cited by 10 publications

References 34 publications

Thermodynamically-guided machine learning modelling for predicting the glass-forming ability of bulk metallic glasses

Thermodynamically-guided machine learning modelling for predicting the glass-forming ability of bulk metallic glasses

Characterization of a Novel System of Bismuth Lead Borosilicate Glass Containing Copper

Synthesis by Mechanical Alloying and Characterization Fe73Si22B5 Alloy: Amorphization Evaluation

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